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In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using 'out of the box' software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research.
Brunsdon and Comber's An Introduction to R for Spatial Analysis and Mapping is a timely text for students concerned with the exploration of spatial analysis problems and their solutions. The authors combine extensive expertise and practical experience with a clear and accessible pedagogic style in the presentation of problems in spatial analysis. This volume is not only an excellent resource for students in the spatial sciences but should also find a place on the bookshelves of researchers.
If you are new to R and spatial analysis, then this is the book for you. With plenty of examples that are easy to use and adapt, there's something for everyone as it moves comfortably from mapping and spatial data handling to more advanced topics such as point-pattern analysis, spatial interpolation, and spatially varying parameter estimation. Of course, all of this is "free" because R is open source and allows anyone to use, modify, and add to its superb functionality.
The statistical sections each use "real" data, and each section ends with "Self-Test Questions". Thus the book is suitable not only as a reference for specific spatial data problems, but also for self-study or for training courses, if you want to approach the topic in principle. Overall, the book has a very successful, rounded overview of the analysis and visualization of spatial data.
Well laid out and easy to follow even for non-technical people.
Plain language; use of R
Too advanced for my introductory class. Recommended to colleague teaching the advanced section.
Open source programs are coming more important in GIS analyses. Commercial GIS-programs does not always provide all essential statistical or spatial tools. In addition, some commercial GIS programs might have expensive licences. Therefore, it is important to have a possibility to utilize free open source programs. The R program is one of these kinds of programs. It has nowadays thousands users globally.
Book was too technical. I will definitely recommend it to students but I am not requiring it.
This book is excellent. Around 50% of the content of my GIS course is taught using R. For many students, this is their first experience of R, and indeed a command-line interface. As such, for many it can be a daunting prospect. I am frequently asked to recommend an introductory textbook and to date, I have not been able to do do this. However, Chris and Lex's book fits the bill perfectly, covering the basics of the R language, handling spatial data, carrying out spatial analyses and working with online data.
"An introduction to R for spatial analysis and mapping" will be used to supplement our courses related to the spatiality of data (in our case: risk). Nice guidebook for the first stept into this very complex software. Recommended as supplementary book for those students willing to work with R.
This is an excellent textbook for both underground and postgraduate students as well as researchers. It is the result of a long research and teaching experience of the authors and should be in every geographer's (student or graduate) bookshelf. The book is well structured, well written with great illustrations and adequate theory just enough for the students to keep reading it. As R is considered top 10 programming language in the world and data analysis is now a growing industry that could employ geography graduates, this textbook became available in the right time.
Excellent guide for beginners to both R and spatial statistics. Practical examples throughout, complete with working code, allow the reader to recreate figures and analyses themselves and learn how to apply techniques to their own data.
Good introduction, but I'll consider this book rather for my advanced Summer School course than for the methods intro at the undergraduate level.
The book is an easy-to-read text for undergraduates and postgraduates who are interested in spatial analysis and cartography. It includes both introductory and advanced topics with working codes for readers to get hands-on experiences of learning by doing. The authors are world leading in this area. I am sure that this book will be of great value not only for students but also researchers in relevant fields as well.
It is a very good book for my students in doing their research project.
This is a great introduction for non-specialists.
Teaching programming in R